Pazzini Renata, Kinouchi Osame, Costa Ariadne A
Universidade de São Paulo, FFCLRP, Departamento de Física, Ribeirão Preto, São Paulo 14040-901, Brazil.
Grupo de Redes Complexas Aplicadas de Jataí, Universidade Federal de Jataí, Jataí, GO 75801-615, Brazil.
Phys Rev E. 2021 Jul;104(1-1):014137. doi: 10.1103/PhysRevE.104.014137.
Networks of stochastic leaky integrate-and-fire neurons, both at the mean-field level and in square lattices, present a continuous absorbing phase transition with power-law neuronal avalanches at the critical point. Here we complement these results showing that small-world Watts-Strogatz networks have mean-field critical exponents for any rewiring probability p>0. For the ring (p=0), the exponents are the same from the dimension d=1 of the directed-percolation class. In the model, firings are stochastic and occur in discrete time steps, based on a sigmoidal firing probability function. Each neuron has a membrane potential that integrates the signals received from its neighbors. The membrane potentials are subject to a leakage parameter. We study topologies with a varied number of neuron connections and different values of the leakage parameter. Results indicate that the dynamic range is larger for p=0. We also study a homeostatic synaptic depression mechanism to self-organize the network towards the critical region. These stochastic oscillations are characteristic of the so-called self-organized quasicriticality.
随机漏电整合-激发神经元网络，无论是在平均场水平还是在方形晶格中，在临界点都呈现出连续的吸收相变，并伴有幂律神经元雪崩。在这里，我们补充这些结果，表明对于任何重连概率p>0的小世界瓦茨-斯托加茨网络，都具有平均场临界指数。对于环（p=0），指数与有向渗流类的维度d=1时相同。在该模型中，放电是随机的，并且基于一个S形放电概率函数在离散时间步长中发生。每个神经元都有一个整合从其邻居接收到的信号的膜电位。膜电位受泄漏参数的影响。我们研究了具有不同数量神经元连接和不同泄漏参数值的拓扑结构。结果表明，p=0时动态范围更大。我们还研究了一种稳态突触抑制机制，以使网络自组织到临界区域。这些随机振荡是所谓自组织准临界性的特征。
